Factors Influencing the Intention to Use Insurance Technology (Insurtech) Among Generation Z Using the Extended D-M Model

Abstract

This study investigates the factors influencing Generation Z’s intention to use Insurtech in Indonesia using an extended DeLone and McLean model. The research introduces two additional variables: perceived trust and regulatory expectancy. Data were collected via an online survey of 431 Generation Z respondents aged 17 and above, residing in ten major Indonesian cities: Jakarta, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Palembang, Medan, Balikpapan, and Makassar, all with a basic understanding of Insurtech. The questionnaire included demographic questions and research variables measured on a five-point Likert scale. Data were analyzed using Structural Equation Modeling (SEM) through Smart PLS 4. Descriptive analysis revealed that most respondents were aged 25-28 years, predominantly female, residing in Jakarta, employed in private sectors, with monthly expenditures below USD 300, and holding a bachelor’s degree. The analysis indicated that respondents viewed Insurtech positively, noting its organized information, flexible services, knowledgeable providers, honest services, and legal protection of personal data. Additionally, respondents expressed a strong interest in using Insurtech soon. The measurement model evaluation confirmed the validity and reliability of all indicators based on convergent validity, discriminant validity, and reliability tests. The structural model analysis showed that the independent variables explained 57% of the variance in intention to use Insurtech and 69% in perceived trust. Hypothesis testing revealed that information quality, system quality, service quality, and regulatory expectancy positively influenced both intentions to use Insurtech and perceived trust. However, contrary to expectations, perceived trust did not significantly affect the intention to use Insurtech. This finding suggests that for Generation Z, trust may be considered a baseline expectation, with factors like system and service quality playing a more direct role in their adoption decisions. Additionally, no significant mediation effects were found. The model demonstrated strong predictive relevance and good fit, confirmed by Q², NFI, and SRMR values.